Channel-wise Autoregressive Entropy Models for Learned Image Compression (2007.08739v1)

Abstract: In learning-based approaches to image compression, codecs are developed by optimizing a computational model to minimize a rate-distortion objective. Currently, the most effective learned image codecs take the form of an entropy-constrained autoencoder with an entropy model that uses both forward and backward adaptation. Forward adaptation makes use of side information and can be efficiently integrated into a deep neural network. In contrast, backward adaptation typically makes predictions based on the causal context of each symbol, which requires serial processing that prevents efficient GPU / TPU utilization. We introduce two enhancements, channel-conditioning and latent residual prediction, that lead to network architectures with better rate-distortion performance than existing context-adaptive models while minimizing serial processing. Empirically, we see an average rate savings of 6.7% on the Kodak image set and 11.4% on the Tecnick image set compared to a context-adaptive baseline model. At low bit rates, where the improvements are most effective, our model saves up to 18% over the baseline and outperforms hand-engineered codecs like BPG by up to 25%.

Overview of Channel-wise Autoregressive Entropy Models for Learned Image Compression

The field of image compression has witnessed significant advancements through the application of deep learning techniques. The paper "Channel-wise Autoregressive Entropy Models for Learned Image Compression," authored by David Minnen and Saurabh Singh, presents an innovative approach to improving rate-distortion (RD) performance in learning-based image codecs. This paper explores the capabilities of channel-conditioning and latent residual prediction (LRP) within neural network architectures, presenting a methodology that enhances RD performance compared to pre-existing context-adaptive models while also minimizing serial processing.

Technical Contributions

In learning-based compression, the goal is to optimize a computational model to minimize a rate-distortion objective. The current state-of-the-art approaches incorporate both forward and backward adaptation in their entropy models. While forward adaptation efficiently integrates into deep networks through side information, backward adaptation, which relies on causal context and serial processing, poses challenges for efficient GPU/TPU usage.

The novel contribution in this paper lies in introducing channel-conditioning and latent residual prediction. The proposed system reshapes the network architecture to leverage parallel processing better and achieve improved rate-distortion trade-offs. The enhancements include:

• Channel-Conditioning (CC): This approach divides the latent tensor along the channel dimension, allowing each segment to be dependent on previously decoded segments. This technique alleviates serial dependency constraints traditionally associated with spatially autoregressive models, thus enhancing parallel processing capabilities.
• Latent Residual Prediction (LRP): This prediction minimizes quantization errors by forecasting the residual errors based on the hyperprior and previously decoded segments. The resultant predictions are added to the quantized latents, benefiting RD performance by reducing entropy through improved parameter conditioning in subsequent segments.

Empirical Evaluation

The paper's empirical analysis demonstrates the model's superior performance on standard datasets. Significant rate savings are noted, with the proposed methods outperforming context-adaptive baseline models by 6.7% on the Kodak dataset and 11.4% on the Tecnick dataset. Notably, at lower bit rates, the improvements yield up to 18% savings over the baseline and up to 25% over manually crafted codecs like BPG.

Implications and Future Research Directions

The proposed method opens up intriguing pathways in optimizing learned image compression techniques. By providing a framework that is inherently more efficient in utilizing modern parallel processing hardware, there is potential for broader applications of these techniques, potentially extending into video compression and beyond.

The results spur further investigation into the balance between model complexity and training efficacy. Future research might explore deeper architectural structures, alternative loss functions, or hybrid approaches that combine the benefits of both channel-conditioning and spatial context modeling. Additionally, understanding how these improvements behave under varying network capacities and quantization strategies can lead to more generalized solutions applicable across different media types and resolutions.

Conclusion

In summary, the integration of channel-conditioning and latent residual prediction provides a noteworthy advancement in the domain of learned image compression. Successfully addressing the inefficiencies of prior models while substantially enhancing RD performance marks a significant achievement in compression methodology. The paper paves a promising path forward, setting a foundation for future exploration and potential applications in compression technologies.